Welcome

Summary

About this Document

This document is an interactive dashboard viewable from most modern internet browsers. The dashboard is a validation and diagnostics tool for CT-RAMP based Activity Based Models. Users can compare model performance against a household survey as part of a validation exercise or compare two model runs for sensitivity testing. All of the data, charts, and maps viewable in this dashboard are embedded directly into the HTML file. An internet connection is necessary for the best user experience, but is not required.

Users may navigate to different areas of the dashboard using the navigation bar at the top of the page, and may interact directly with most tables, charts, and maps.

This document is best viewed using the most recent versions of the following web browsers:

Note: Mozilla Firefox does not correctly render the images in this HTML file.

Summary

Modeling Region

Overview

Base Highlights

2010_R1

Base Population

7,141,990

Base Households

2,608,015

Base Tours

8,434,025

Base Trips

21,912,600

Base Stops

5,044,550

Base VMT

91,006,016

Build Highlights

2010_R2

Build Population

7,131,397

Build Households

2,695,461

Build Tours

7,883,102

Build Trips

19,818,256

Build Stops

4,052,052

Build VMT

72,590,036

Chart Column 1

Person Type Distribution

Household Size Distribution

Base Highlights2

2010_R1

Tours per Person

Trips per Person

Stops per Person

Trips per Household

Build Highlights2

2010_R2

Tours per Person

Trips per Person

Stops per Person

Trips per Household

Long Term Models

Chart Column 1

Auto Ownership

Census source:  2010_R1

Working from home:  CHTS  vs.  2010_R2

Percentage Working From Home

Chart Column 2

Mandatory TLFD

Flows & Tour Lengths

Chart Column 1

District - District Flow of Workers
2010_R1
X Alameda Contra.Costa Marin Napa San.Francisco San.Mateo Santa.Clara Solano Sonoma Total
Alameda 391,830 46,690 4,980 500 64,285 33,660 76,920 2,760 835 622,460
Contra Costa 100,510 231,985 7,860 2,505 38,355 8,855 12,335 14,225 1,715 418,345
Marin 13,685 8,480 37,620 820 31,530 4,380 1,400 1,630 5,675 105,220
Napa 2,740 4,695 1,335 28,780 1,610 620 1,450 7,260 4,965 53,455
San Francisco 39,670 10,470 7,995 235 290,325 32,735 6,040 995 745 389,210
San Mateo 39,680 5,855 3,415 225 72,130 132,615 58,140 540 400 313,000
Santa Clara 59,720 5,645 680 205 8,235 38,685 621,725 715 420 736,030
Solano 13,320 28,670 3,070 8,560 7,380 2,075 3,580 96,335 2,610 165,600
Sonoma 4,395 3,390 8,770 5,445 5,805 1,435 1,680 2,620 164,125 197,665
Total 665,550 345,880 75,725 47,275 519,655 255,060 783,270 127,080 181,490 3,000,985

Average Mandatory Tour Lengths
2010_R1
Home District Work University School
Alameda 12.29 6.98 1.23
Contra Costa 15.40 10.22 2.42
Marin 17.28 12.83 2.53
Napa 17.60 8.70 2.81
San Francisco 7.12 4.63 1.43
San Mateo 13.61 9.01 1.95
Santa Clara 11.03 7.35 2.37
Solano 17.40 11.77 2.79
Sonoma 13.62 9.05 2.63
Total 12.52 7.86 2.09

Chart Column 1

District-District Flow of Workers
2010_R2
X Alameda Contra.Costa Marin Napa San.Francisco San.Mateo Santa.Clara Solano Sonoma Total
Alameda 432,990 33,712 3,288 516 45,688 24,806 60,807 2,047 701 604,555
Contra Costa 90,398 250,225 5,947 2,384 28,505 6,056 11,237 13,183 1,274 409,209
Marin 10,748 5,909 49,489 590 24,248 3,439 1,386 1,232 5,114 102,155
Napa 2,654 3,689 908 34,008 1,567 462 566 7,639 3,731 55,224
San Francisco 11,416 2,234 1,575 49 350,113 12,301 1,688 208 170 379,754
San Mateo 30,138 4,139 2,286 197 66,638 153,767 53,404 483 358 311,410
Santa Clara 49,249 4,621 661 226 5,747 30,655 631,800 536 310 723,805
Solano 11,698 22,701 2,179 7,575 5,521 1,799 2,097 104,918 2,168 160,656
Sonoma 3,631 2,368 6,397 4,471 4,263 1,232 1,511 2,187 167,052 193,112
Total 642,922 329,598 72,730 50,016 532,290 234,517 764,496 132,433 180,878 2,939,880

Average Mandatory Tour Lengths
2010_R2
Home District Work University School
Alameda 9.89 6.33 1.08
Contra Costa 13.45 9.96 2.25
Marin 14.31 12.25 2.26
Napa 13.50 7.48 2.27
San Francisco 4.41 3.88 1.68
San Mateo 11.53 9.33 1.84
Santa Clara 9.51 6.69 2.32
Solano 14.27 11.81 2.05
Sonoma 11.41 7.80 2.07
Total 10.32 7.16 1.91

Tour Summaries

Chart Column 1

Daily Activity Pattern

Percentage of Households with a Joint Tour

Mandatory Tour Frequency

Chart Column 1

Total Tour Rate (only active Persons)

Persons by Individual Non-Mandatory Tours

Joint Tours

Chart Column 1

Joint Tour Frequency

Joint Tour Composition

Chart Column 1

Joint Tours By Number of Household Members

Joint Tours by Household Size

Party Size Distribution by Joint Tour Composition

Destination

Chart Column 1

Non-Mandatory Tour Length Distribution

Average Non-Mandatory Tour Lengths (Miles)

Purpose 2010_R1 2010_R2
Escorting 2.61 5.06
Indi-Maintenance 4.89 5.47
Indi-Discretionary 4.83 7.37
Joint-Maintenance 4.91 4.67
Joint-Discretionary 5.17 7.31
At-Work 2.19 2.12
Total 6.74 6.58

TOD

Chart Column 1

Tour Departure-Arrival Profile

Tour Aggregate Departure-Arrival Profile

Tour Mode

Chart Column 1

Tour Mode Choice


Tour Mode Choice

Results of Tour Mode Choice Models, which selects a primary mode for each tour.

Distribution of tours by tour mode and the ratio of autos to drivers in the household.

Chart Column 2

Chart Column 3

Stop Frequency

Chart Column 1

Stop Frequency - Directional

Chart Column 1

Stop Frequency - Total

Stop Purpose by Tour Purpose

Location

Chart Column 1

Stop Location - Out of Direction Distance

Chart Column 1

Average Out of Direction Distance (Miles)

_______________________________________________________
Tour_Purpose 2010_R1 2010_R2
Work 3.64 1.78
University 2.29 1.75
School 2.65 7.82
Escorting 3.60 2.44
Indi-Maintenance 2.49 2.22
Indi-Discretionary 1.96 1.56
Joint-Maintenance 3.45 3.17
Joint-Discretionary 2.81 2.42
At-Work 3.18 1.78

TOD

Chart Column 1

Stop & Trip Departure

Aggregate Stop & Trip Departure

Trip Mode

Chart Column 1

Trip Mode Choice

The results of the Trip Mode Choice Model, which predicts the mode of each trip on the tour.

Distribution of trips by trip mode and tour mode, which constrains the availability of each trip mode and influences the utility of each available trip mode.

Trip Mode Choice

Chart Column 2

Count vs Volume

Chart Column 2

Count vs Volume by Facility Type

Gap Statistics

---
title: "`r paste(BASE_SCENARIO_NAME, 'vs.', BUILD_SCENARIO_NAME, 'Calibration Summary')`"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    theme: spacelab
    social: menu
    source_code: embed
---

```{r Setup}
opts_knit$set(root.dir = SYSTEM_APP_PATH)
```

```{r setpar}
knitr::opts_knit$set(global.par = TRUE)
```

```{r ggplot_Theme}
theme_db <- theme_bw() + theme(plot.margin = unit(c(10,10,20,10),"pt")) 
```

```{r Helper_Functions}
compare_bar_plotter <- function(base, build, base_name, build_name, xvar, yvar, 
                        xlabel = xvar, ylabel = yvar, position = "dodge", 
                        xrotate = FALSE, yrotate = FALSE, coord_flip = FALSE, 
                        title = "", left_offset = 0, bottom_offset = 0){
  
  base$grp <- base_name
  build$grp <- build_name
  colnames(build) <- colnames(base)
  
  df <- rbind(base, build)
  
  p <- ggplot(df, aes_string(x = xvar, y = yvar)) + 
    geom_bar(stat = "identity", aes(fill = grp), position = position) + 
    xlab(xlabel) + ylab(ylabel) +
    labs(fill = "") + 
    ggtitle(title) + 
    theme(axis.text.x=element_text(angle=50, size=1, vjust=0.5)) + 
    theme(axis.text.y=element_text(angle=50, size=1, vjust=0.5)) + 
    theme_bw()
  
  if (xrotate) {
    p <- p + theme(axis.text.x = element_text(angle = 45, hjust = 1))
  }
  if (yrotate) {
    p <- p + theme(axis.text.y = element_text(angle = 45, hjust = 1))
  }
  if (coord_flip) {
    p <- p + coord_flip()
  }
  
 
  p <- plotly_build(p)
  p$layout$margin$l <- p$layout$margin$l+left_offset
  p$layout$margin$b <- p$layout$margin$b+bottom_offset
  return(p)
  
}

# This function combines two dataframes and returns a data frame with standard field names
# The field names in the two dataframes should be same and should be same as the variable
# names passed to the function
# input parameter - dataframe1, dataframe2, x variable, list of y variables
# renames x and y variables in standard form - xvar, (yvar1, yvar2),...
# Y variables are named in pairs - (yvar1, yvar2), (yvar3, yvar4), ....
# yvar1, yvar3, .. correspond to first dataframe and yvar2, yvar4, .. correspond to second dataframe
# computes proportions for each  y variable variable
get_standardDF <- function(data_df1, data_df2, x, y, grp = "", shared = F){
  
  #data_df1=base_df
  #data_df2=build_df
  #x="id"
  #y = c("freq_out", "freq_inb")
  #grp = "purpose"
  #shared = T
  #
  # create ID variable to join base and build data
  if(!shared){
    ev1 <- paste("data_df1$id_var <- data_df1$", x, sep = "")
    ev2 <- paste("data_df2$id_var <- data_df2$", x, sep = "")
    eval(parse(text = ev1))
    eval(parse(text = ev2))
  }else{
    if(grp==""){
      stop("group variable not specified")
    }else{
      ev1 <- paste("data_df1$id_var <- paste(data_df1$", grp, ", data_df1$", x, ', sep = "")', sep = "")
      ev2 <- paste("data_df2$id_var <- paste(data_df2$", grp, ", data_df2$", x, ', sep = "")', sep = "")
      eval(parse(text = ev1))
      eval(parse(text = ev2))
    }
  }
  
  data_df <- data_df1
  
  # rename variables to standard names
  names(data_df)[names(data_df) == x] <- 'xvar'
  if(shared){
    if(grp==""){
      stop("group variable not specified")
    }else{
      names(data_df)[names(data_df) == grp] <- 'grp_var'
    }
  }
  
  for(i in seq(from=1, to=length(y))){
    start_pos <- i*2-1
    yvar1 <- paste('yvar', start_pos, sep = "")
    yvar2 <- paste('yvar', start_pos+1, sep = "")
    names(data_df)[names(data_df) == y[[i]]] <- paste('yvar', start_pos, sep = "")
    eval_expr <- paste("data_df$", yvar2, " <- data_df2$", y[[i]], "[match(data_df$id_var, data_df2$id_var)]", sep = "")
    eval(parse(text = eval_expr))
  }
  data_df[is.na(data_df)] <- 0
  
  #data_df$grp_var <- as.character(data_df$grp_var)
  
  # compute proportions for y variables
  for(i in seq(from=1, to=length(y))){
    start_pos <- i*2-1
    prop_var1 <- paste('prop', start_pos, sep = "")
    y_var1    <- paste('yvar', start_pos, sep = "")
    prop_var2 <- paste('prop', start_pos+1, sep = "")
    y_var2    <- paste('yvar', start_pos+1, sep = "")
    if(shared){
      if(grp==""){
        stop("group variable not specified")
      }else{
        eval_expr1 <- paste("data_df <- data_df %>% group_by(grp_var) %>% mutate(", prop_var1, " = prop.table(", y_var1, "))", sep = "")
        eval_expr2 <- paste("data_df <- data_df %>% group_by(grp_var) %>% mutate(", prop_var2, " = prop.table(", y_var2, "))", sep = "")
      }
    }else{
      eval_expr1 <- paste("data_df <- data_df %>% mutate(", prop_var1, " = prop.table(", y_var1, "))", sep = "")
      eval_expr2 <- paste("data_df <- data_df %>% mutate(", prop_var2, " = prop.table(", y_var2, "))", sep = "")
    }
    
    eval(parse(text = eval_expr1))
    eval(parse(text = eval_expr2))
  }
  
  # set all NAs to zero
  data_df[is.na(data_df)] <- 0
  
  if(!shared){
    return(data_df)
  }else{
    data_sd <- SharedData$new(data_df, ~grp_var)
    return(data_sd)
  }
}

# This function returns a SharedData object for creating comparison density plots
# input parameter - dataframe1, dataframe2, x variable, list of y variables, 
# grouping variable, names of each run
# The function expects same field names across both dataframes
# renames x and y variables in standard form - xvar, yvar1, yvar2,...
# computes proportions for each  y variable variable for each group and run
# combines two dataframe and adds a run identifier
get_sharedData <- function(data_df1, data_df2, run1_name = 'base', run2_name = 'build', 
                           x, y, grp){
  
  # rename variables to standard names
  names(data_df1)[names(data_df1) == x] <- 'xvar'
  names(data_df1)[names(data_df1) == grp] <- 'grp_var'
  for(i in 1:length(y)){
    names(data_df1)[names(data_df1) == y[[i]]] <- paste('yvar', i, sep = "")
  }
  
  names(data_df2)[names(data_df2) == x] <- 'xvar'
  names(data_df2)[names(data_df2) == grp] <- 'grp_var'
  for(i in 1:length(y)){
    names(data_df2)[names(data_df2) == y[[i]]] <- paste('yvar', i, sep = "")
  }
  
  # compute proportions for y variables
  data_df1 <- group_by(data_df1, grp_var)
  for(i in 1:length(y)){
    prop_var <- paste('prop', i, sep = "")
    y_var    <- paste('yvar', i, sep = "")
    eval_expr <- paste("data_df1 <- data_df1 %>% mutate(", prop_var, " = prop.table(", y_var, "))", sep = "")
    eval(parse(text = eval_expr))
  }
  
  data_df2 <- group_by(data_df2, grp_var)
  for(i in 1:length(y)){
    prop_var <- paste('prop', i, sep = "")
    y_var    <- paste('yvar', i, sep = "")
    eval_expr <- paste("data_df2 <- data_df2 %>% mutate(", prop_var, " = prop.table(", y_var, "))", sep = "")
    eval(parse(text = eval_expr))
  }
  
  # add run identifiers
  data_df1$run <- run1_name
  data_df2$run <- run2_name
  
  # combine dataframes
  data_df <- rbind(data_df1, data_df2)
  
  # set all NAs to zero
  data_df[is.na(data_df)] <- 0
  
  data_sd <- SharedData$new(data_df, ~grp_var)
  return(data_sd)
}

# This function returns bar plot for a given X-Y data frame
# The function expects the data frame columns to be named as
# xvar, yvar1, yvar2...
# function plots only two series at a time
# which two y series to plot is determined by the index variable
# index==1 :- yvar1, yvar2, index==2 :- yvar,3,4 and so on
# names of series to be plotted should also be passed as a list argument
# number of elements in names list determines the number of series to be added 
plotly_bar_plotter <- function(data, type = 'bar', xlabel = "", ylabel = "", percent = FALSE,
                               title = "", height = 0, width = 0, ynames = c(""), left_offset = 0, 
                               bottom_offset = 0, tickformat = "", hoverformat = "", tickangle = 0, index = 1, tickvals = c(), ticktext = c()){
  #initial setup
  start_pos <- 2*index - 1
  exp_tickvals <- ifelse(length(tickvals)>0, ', tickvals = tickvals', "")
  exp_ticktext <- ifelse(length(ticktext)>0, ', ticktext = ticktext', "")
  
  #generate plot
  if(!percent){
    ylab <- ifelse(ylabel=="", "Percent", ylabel)
    hformat <- ifelse(hoverformat=="", '.1f', hoverformat)
    eval_expr <- paste("p <- plot_ly(data, x = ~xvar, y = ~yvar", start_pos, ", type = type, name = ynames[[1]]) %>% ", 
                       "add_trace(y = ~yvar", start_pos+1, ", name = ynames[[2]]) %>% ", 
                       "layout(yaxis = list(hoverformat = hformat, title = ylab, tickformat = tickformat), xaxis = list(title = xlabel, tickangle = tickangle", exp_tickvals, exp_ticktext, "), barmode = 'group')", sep = "")
    eval(parse(text = eval_expr))
  }else{
    ylab <- ifelse(ylabel=="", "Frequency", ylabel)
    hformat <- ifelse(hoverformat=="", '.1%', hoverformat)
    eval_expr <- paste("p <- plot_ly(data, x = ~xvar, y = ~prop", start_pos, ", type = type, name = ynames[[1]]) %>% ", 
                       "add_trace(y = ~prop", start_pos+1, ", name = ynames[[2]]) %>% ", 
                       "layout(yaxis = list(hoverformat = hformat, title = ylab, tickformat = '%'), xaxis = list(title = xlabel, tickangle = tickangle", exp_tickvals, exp_ticktext,"), barmode = 'group')", sep = "")
    eval(parse(text = eval_expr))
  }
  
  p$x$layout$height <- height
  p$x$layout$width <- width
  p$x$layout$margin$b <- p$x$layout$margin$b + bottom_offset
  p$x$layout$margin$l <- p$x$layout$margin$l + left_offset
  return(p)
}

# This function returns a spline plot with fill for a gievn X-Y dataframe
# The function expects the data frame columns to be named as
# x = ~xvar, y = (~yvar1 or prop1),  (~yvar2 or prop2) adn so on (Frequency or Percent), 
# which y to use is determined by index parameter (one, two or three)
# and variable differentiating runs as ~run
# The function currebtly plots only one Y variables for each run
plotly_density_plotter <- function(data_df, index = "one", colors=c("orange", "steelblue"), fill = 'tozeroy', 
                                   title = "", xlabel = "", ylabel = "", percent = T, alpha = 0.7, tickvals, ticktext, tickangle = 0,
                                   height=400, left_offset = 0, bottom_offset = 0, shape = 'spline', legend = T){
  ##standardize data frame
  #names(data_df)[names(data_df)==xvar]     <- 'xvar'
  #names(data_df)[names(data_df)==yvar]     <- 'yvar1'
  #names(data_df)[names(data_df)==prop_var] <- 'prop1'
  #names(data_df)[names(data_df)==grp]      <- 'run'
  
  # prepare plot using standardized dataframe
  if(percent){
    ylab <- ifelse(ylabel=="", "Percent", ylabel)
    
    p <- switch(index, 
                "one" = plot_ly(data=data_df,x = ~xvar, y = ~prop1, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name="",alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab, tickformat = "%"), showlegend = legend),
                "two" = plot_ly(data=data_df,x = ~xvar, y = ~prop2, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name="",alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab, tickformat = "%"), showlegend = legend),
                "three" = plot_ly(data=data_df,x = ~xvar, y = ~prop3, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name="",alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab, tickformat = "%"), showlegend = legend)
                )
    
  }else{
    ylab <- ifelse(ylabel=="", "Frequency", ylabel)
    
    p <- switch(index,
                "one" = plot_ly(data=data_df,x = ~xvar, y = ~yvar1, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name="",alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab), showlegend = legend),
                "two" = plot_ly(data=data_df,x = ~xvar, y = ~yvar2, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name="",alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab), showlegend = legend),
                "three" = plot_ly(data=data_df,x = ~xvar, y = ~yvar3, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name="",alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab), showlegend = legend)
                )
    
    #p <- plot_ly(data=data_df,x = ~xvar, y = ~yvar1, colors=c("steelblue", "orange"), color = ~run, height=400, fill=fill) %>%
    #add_lines(name="",alpha=alpha, line = list(shape = shape)) %>% 
    #layout(title = "",xaxis = list(title=xlabel), yaxis = list(title=ylab))
  }
  
  p$x$layout$height <- height
  p$x$layout$margin$b <- p$x$layout$margin$b + bottom_offset
  p$x$layout$margin$l <- p$x$layout$margin$l + left_offset
  return(p)
}

# This function returns a pie chart
# Input is a 2 column data frame with a label variable and a value variable
plotly_pie_chart <- function(data, label_var, value_var, title = "", 
                               height = 0, width = 0, left_offset = 0,bottom_offset = 0, top_offset = 0, shared = F){
  
  colors <- c('rgb(211,94,96)', 'rgb(128,133,133)', 'rgb(144,103,167)', 'rgb(171,104,87)', 'rgb(114,147,203)')
  
  if(!shared){
    names(data)[names(data)==label_var] <- 'label_var'
    names(data)[names(data)==value_var] <- 'value_var'
    
    p <- plot_ly(data, labels = ~label_var, values = ~value_var, type = 'pie',
          textposition = 'outside',
          textinfo = 'label+percent',
          insidetextfont = list(color = '#FFFFFF'),
          marker = list(colors = colors,
                        line = list(color = '#FFFFFF', width = 2)),
          showlegend = FALSE, 
          sort = FALSE) %>%
    layout(title = title,
           xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
           yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
  }else{
    eval_expr <- paste("p <- plot_ly(data, labels = ~", label_var, ", values = ~", value_var, ", type = 'pie',
          textposition = 'outside',
          textinfo = 'label+percent',
          insidetextfont = list(color = '#FFFFFF'),
          marker = list(colors = colors,
                        line = list(color = '#FFFFFF', width = 2)),
          showlegend = FALSE, 
          sort = FALSE) %>%
    layout(title = title,
           xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
           yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))", sep = "")
    
    eval(parse(text = eval_expr))
  }
  
  
  p$x$layout$height <- height
  p$x$layout$width <- width
  p$x$layout$margin$b <- p$x$layout$margin$b + bottom_offset
  p$x$layout$margin$l <- p$x$layout$margin$l + left_offset
  p$x$layout$margin$t <- p$x$layout$margin$t + top_offset
  return(p)
}

lm_eqn <- function(df){
    m <- lm(y ~ x - 1, df);
    eq <- paste("Y = ", format(coef(m)[1], digits = 2), " * X , ", " r2 = ", format(summary(m)$r.squared, digits = 3), sep = "")
    return(eq)
}

```

Welcome
============================================

Summary {data-width=150}
--------------------------------------------

### About this Document

This document is an interactive dashboard viewable from most modern internet browsers. The dashboard is a validation and diagnostics tool for CT-RAMP based Activity Based Models. Users can compare model performance against a household survey as part of a validation exercise or compare two model runs for sensitivity testing. All of the data, charts, and maps viewable in this dashboard are embedded directly into the HTML file. An internet connection is necessary for the best user experience, but is not required.

Users may navigate to different areas of the dashboard using the navigation bar at the top of the page, and may interact directly with most tables, charts, and maps.

This document is best viewed using the most recent versions of the following web browsers:

* [Google Chrome](https://www.google.com/chrome/browser/desktop/)
* [Microsoft Internet Explorer](https://www.microsoft.com/en-us/download/internet-explorer.aspx)

Note: Mozilla Firefox does not correctly render the images in this HTML file.

Summary {data-width=600}
--------------------------------------------

### Modeling Region
```{r model_region}
bins <- c(0, 10, 20, 50, 100, 200, 500, 1000, Inf)
pal <- colorBin("YlOrRd", domain = zone_shp$HH, bins = bins)

m <- leaflet(data = zone_shp)%>% 
  addTiles() %>% 
  addProviderTiles(providers$OpenStreetMap, group = "Background Map") %>%
  addLayersControl(
    overlayGroups = "Background Map", options = layersControlOptions(collapsed = FALSE)
  ) %>%
  addPolygons(weight = 0.5, opacity = 1)
m

#  
```


Overview
============================================

Base Highlights {data-width=90}
--------------------------------------------

### 

```{r Run_Date1_ValueBox}
sample_rate <- ifelse(IS_BASE_SURVEY=="Yes", "", as.character(BASE_SAMPLE_RATE*100))
valueBox(BASE_SCENARIO_NAME, paste("Sample Rate: ", sample_rate, "%", sep = ""), color = "DarkOrange")
base_pos <- which(base_csv_names=="totals")
base_df <- base_data[[base_pos]]
```

### Base Population
```{r Population1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_population"]/BASE_SAMPLE_RATE), big.mark = ","), "Population", icon = "ion-ios-people")
```

### Base Households
```{r Household1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_households"]/BASE_SAMPLE_RATE), big.mark = ","), "Households", icon = "glyphicon glyphicon-home")
```

### Base Tours
```{r Tours1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_tours"]/BASE_SAMPLE_RATE), big.mark = ","), "Total Tours", icon = "ion-refresh")
```

### Base Trips
```{r Trips1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_trips"]/BASE_SAMPLE_RATE), big.mark = ","), "Total Trips", icon = "ion-loop")
```

### Base Stops
```{r Stops1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_stops"]/BASE_SAMPLE_RATE), big.mark = ","), "Total Stops", icon = "ion-ios-location")
```

### Base VMT
```{r VMT1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_vmt"]/BASE_SAMPLE_RATE), big.mark = ","), "Total VMT", icon = "ion-android-car")
```



Build Highlights {data-width=90}
--------------------------------------------

### 

```{r Run_Date2_ValueBox}
valueBox(BUILD_SCENARIO_NAME, paste("Sample Rate: ", BUILD_SAMPLE_RATE*100, "%", sep = ""), color = "DarkOrange")
build_pos <- which(build_csv_names=="totals")
build_df <- build_data[[build_pos]]
```

### Build Population
```{r Population2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_population"]/BUILD_SAMPLE_RATE), big.mark = ","), "Population", icon = "ion-ios-people")
```

### Build Households
```{r Household2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_households"]/BUILD_SAMPLE_RATE), big.mark = ","), "Households", icon = "glyphicon glyphicon-home")
```

### Build Tours
```{r Tours2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_tours"]/BUILD_SAMPLE_RATE), big.mark = ","), "Total Tours", icon = "ion-refresh")
```

### Build Trips
```{r Trips2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_trips"]/BUILD_SAMPLE_RATE), big.mark = ","), "Total Trips", icon = "ion-loop")
```

### Build Stops
```{r Stops2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_stops"]/BUILD_SAMPLE_RATE), big.mark = ","), "Total Stops", icon = "ion-ios-location")
```

### Build VMT
```{r VMT2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_vmt"]/BUILD_SAMPLE_RATE), big.mark = ","), "Total VMT", icon = "ion-android-car")
```


Chart Column 1 {data-width=275}
--------------------------------------------
### Person Type Distribution
```{r Chart_Person_Type}
base_pos <- which(base_csv_names=="pertypeDistbn")
base_df <- base_data[[base_pos]]
base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)]
base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char)
build_pos <- which(build_csv_names=="pertypeDistbn")
build_df <- build_data[[build_pos]]
build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)]
build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char)

colnames(build_df) <- colnames(base_df)

std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "PERNAME", y = c("freq"))
p <- plotly_bar_plotter(data = std_DF, xlabel = "Person Type", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, bottom_offset = 60, tickangle = -30)
p

```

### Household Size Distribution
```{r Chart_HHSize}
base_pos <- which(base_csv_names=="hhSizeDist")
base_df <- base_data[[base_pos]]
build_pos <- which(build_csv_names=="hhSizeDist")
build_df <- build_data[[build_pos]]

colnames(build_df) <- colnames(base_df)

std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "HHSIZE", y = c("freq"))
p <- plotly_bar_plotter(data = std_DF, xlabel = "HH Size", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T)
p

```

Base Highlights2 {data-width=90}
--------------------------------------------

### 

```{r Run_Date3_ValueBox}
valueBox(BASE_SCENARIO_NAME, "", color = "DarkOrange")
base_pos <- which(base_csv_names=="totals")
base_df <- base_data[[base_pos]]
```


### Tours per Person
```{r TourRate3_Gauge}
rate <- base_df$value[base_df$name=="total_tours"]/base_df$value[base_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 2, gaugeSectors(danger = c(0,2), colors = c("Green", "Green", "Green")))
```

### Trips per Person
```{r TripRate3_Gauge}
rate <- base_df$value[base_df$name=="total_trips"]/base_df$value[base_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 5, gaugeSectors(danger = c(0,5), colors = c("Green", "Green", "Green")))
```

### Stops per Person
```{r StopRate3_Gauge}
rate <- base_df$value[base_df$name=="total_stops"]/base_df$value[base_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 2, gaugeSectors(danger = c(0,2), colors = c("Green", "Green", "Green")))
```

### Trips per Household
```{r TRate3_Gauge}
rate <- base_df$value[base_df$name=="total_trips"]/base_df$value[base_df$name=="total_households"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 15, gaugeSectors(danger = c(0,15), colors = c("Green", "Green", "Green")))
```


Build Highlights2 {data-width=90}
--------------------------------------------

### 

```{r Run_Date4_ValueBox}
valueBox(BUILD_SCENARIO_NAME, "", color = "DarkOrange")
build_pos <- which(build_csv_names=="totals")
build_df <- build_data[[build_pos]]
```


### Tours per Person
```{r TourRate4_Gauge}
rate <- build_df$value[build_df$name=="total_tours"]/build_df$value[build_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 2, gaugeSectors(danger =  c(0,2), colors = c("Green", "Green", "Green")))
```

### Trips per Person
```{r TripRate4_Gauge}
rate <- build_df$value[build_df$name=="total_trips"]/build_df$value[build_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 5, gaugeSectors(danger = c(0,5), colors = c("Green", "Green", "Green")))
```

### Stops per Person
```{r StopRate4_Gauge}
rate <- build_df$value[build_df$name=="total_stops"]/build_df$value[build_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 2, gaugeSectors(danger = c(0,2), colors = c("Green", "Green", "Green")))
```

### Trips per Household
```{r TRate4_Gauge}
rate <- build_df$value[build_df$name=="total_trips"]/build_df$value[build_df$name=="total_households"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 15, gaugeSectors(danger = c(0,15), colors = c("Green", "Green", "Green")))
```


Long Term Models{data-navmenu="Long Term"}
============================================

Description {.sidebar data-width=225}
--------------------------------------------


**Auto Ownership**

Results of household auto ownership model, which predicts number of vehicles per household.

**Work from Home**

Result of work from home choice model, which predicts whether workers have usual work place at home. These workers do not generate work tours, but can have non-mandatory tours.

**Mandatory TLFD**

Results of work and school location choice models.

Distribution of workers by distance between home and usual work place, and students by distance between home and school location.

Chart Column 1 {data-width=200}
--------------------------------------------
### Auto Ownership{data-height=265}
```{r Chart_Auto_Ownership}
cat("Census source: ", AO_CENSUS_LONG)
base_pos <- which(base_csv_names=="autoOwnership")
base_df <- base_data[[base_pos]]
build_pos <- which(build_csv_names=="autoOwnership")
build_df <- build_data[[build_pos]]

colnames(build_df) <- colnames(base_df)

std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "HHVEH", y = c("freq"))

p <- plotly_bar_plotter(data = std_DF, xlabel = "Number of Vehicles", ylabel = "Percent", ynames = c(AO_CENSUS_SHORT, BUILD_SCENARIO_NAME), percent = T, height = 225)
p
```

### {data-height=140}
```{r Gauge_WFH1}
cat("Working from home: ", WFH_Source, " vs. ", BUILD_SCENARIO_NAME)

base_df <- base_data[[which(base_csv_names=="wfh_summary_region")]]
rate <- base_df$WFH/base_df$Workers
gauge1 <- gauge(round(rate*100, 1), min = 0, max = 100, symbol = '%', gaugeSectors(danger =  c(0,1), colors = c("Green", "Green", "Green")))

build_df <- build_data[[which(build_csv_names=="wfh_summary")]]
rate <- build_df$WFH[build_df$District=="Total"]/build_df$Workers[build_df$District=="Total"]
gauge2 <- gauge(round(rate*100, 1), min = 0, max = 100, symbol = '%', gaugeSectors(danger =  c(0,1), colors = c("Green", "Green", "Green")))

bscols(widths = c(6,6),
  gauge1,
  gauge2
)

```

### Percentage Working From Home{data-height=250}
```{r Chart_WFH}
base_df <- base_data[[which(base_csv_names=="wfh_summary")]]
base_df$share <- base_df$WFH/base_df$Workers

build_df <- build_data[[which(build_csv_names=="wfh_summary")]]
build_df$share <- build_df$WFH/build_df$Workers

std_DF <- cbind(base_df[,c("District", "share")], build_df[,c("share")])
colnames(std_DF) <- c("xvar", "prop1", "prop2")

p <- plotly_bar_plotter(data = std_DF, xlabel = "District", ylabel = "Percent WFH", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, height = 275, tickangle = -320, bottom_offset = 25)
p

```



Chart Column 2 {data-width=350}
--------------------------------------------


### Mandatory TLFD{data-height=475}
```{r mandatoryTLFD}
base_df1 <- base_data[[which(base_csv_names=="workTLFD")]]
base_df1 <- melt(base_df1, id = c("distbin"))

base_df2 <- base_data[[which(base_csv_names=="univTLFD")]]
base_df2 <- melt(base_df2, id = c("distbin"))

base_df3 <- base_data[[which(base_csv_names=="schlTLFD")]]
base_df3 <- melt(base_df3, id = c("distbin"))

base_df <- cbind(base_df1, base_df2$value, base_df3$value)
colnames(base_df) <- c("distbin","variable","value1","value2","value3")

build_df1 <- build_data[[which(build_csv_names=="workTLFD")]]
build_df1 <- melt(build_df1, id = c("distbin"))

build_df2 <- build_data[[which(build_csv_names=="univTLFD")]]
build_df2 <- melt(build_df2, id = c("distbin"))

build_df3 <- build_data[[which(build_csv_names=="schlTLFD")]]
build_df3 <- melt(build_df3, id = c("distbin"))

build_df <- cbind(build_df1, build_df2$value, build_df3$value)
colnames(build_df) <- c("distbin","variable","value1","value2","value3")

sd.purpose <- get_sharedData(data_df1 = build_df, data_df2 = base_df, run1_name = BUILD_SCENARIO_NAME, 
                             run2_name = BASE_SCENARIO_NAME, x = "distbin", y = c("value1", "value2", "value3"), grp = "variable")

p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Miles to Work", percent = T, tickvals = seq(1,50,5), ticktext = seq(0,50,5), height = 240)
p2 <- plotly_density_plotter(sd.purpose, index = "two", xlabel = "Miles to University", percent = T, tickvals = seq(1,50,5), ticktext = seq(0,50,5), height = 240)
p3 <- plotly_density_plotter(sd.purpose, index = "three", xlabel = "Miles to School", percent = T, tickvals = seq(1,50,5), ticktext = seq(0,50,5), height = 240)
	
bscols(widths=c(12),
  list(filter_select("Purpose_County", "Select District", sd.purpose, ~grp_var,multiple=F),
  p1,
  p2,
  p3)
  )

```


Flows & Tour Lengths{data-navmenu="Long Term"}
============================================

Description {.sidebar data-width=150}
--------------------------------------------

**District-District Flow of Workers**

Crosstab of workers by home county and usual work place county.

Note: Districts can be Tract, County, District etc.

**Average Tour Lengths**

Average tour length to workplace by District of residence



Chart Column 1
--------------------------------------------

###{data-height=300}
```{r Table1_CountyFlows}
cat("District - District Flow of Workers")

base_pos <- which(base_csv_names=="countyFlows")
base_df <- base_data[[base_pos]]
base_df[,!colnames(base_df) %in% c("X")] <- base_df[,!colnames(base_df) %in% c("X")]/BASE_SAMPLE_RATE
t1 <- kable(base_df, format = 'html', caption = DISTRICT_FLOW_CENSUS, digits = 0, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10)
t1
```

### {data-height=280}
```{r Table1_MandTripLengths}
cat("Average Mandatory Tour Lengths")

base_df <- base_data[[which(base_csv_names=="mandTripLengths")]]
df <- base_df
colnames(df) <- c("Home District", "Work","University","School")

eval_expr <- paste("t1 <- kable(df, format = 'html', digits = 2, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10, full_width=F, position='center') %>%
  add_header_above(c(' ' = 1, '", BASE_SCENARIO_NAME, "' = 3))", sep = "")
eval(parse(text = eval_expr))
t1
```


Chart Column 1 
--------------------------------------------
###{data-height=300} 
```{r Table2_CountyFlows}
cat("District-District Flow of Workers")

build_pos <- which(build_csv_names=="countyFlows")
build_df <- build_data[[build_pos]]
build_df[,!colnames(build_df) %in% c("X")] <- build_df[,!colnames(build_df) %in% c("X")]/BUILD_SAMPLE_RATE
t2 <- kable(build_df, format = 'html', caption = BUILD_SCENARIO_NAME, digits = 0, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10)
t2
```

###{data-height=280}
```{r Table2_MandTripLengths}
cat("Average Mandatory Tour Lengths")

build_df <- build_data[[which(build_csv_names=="mandTripLengths")]]
df <- build_df
colnames(df) <- c("Home District", "Work","University","School")

eval_expr <- paste("t2 <- kable(df, format = 'html', digits = 2, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10, full_width=F, position='center') %>%
  add_header_above(c(' ' = 1, '", BUILD_SCENARIO_NAME, "' = 3))", sep = "")
eval(parse(text = eval_expr))
t2
```




Tour Summaries{data-navmenu="Tour Level"}
============================================

Description {.sidebar data-width=225}
--------------------------------------------

This page summarizes day-pattern and tour generation model results.

**Daily Activity Pattern**

Results of Coordinated Daily Activity Pattern (CDAP) model, summarized for each person.

_M_ : One or more mandatory tours

_N_ : No mandatory tours but one or more non-mandatory tours

_H_ : No tours (either home all day or out of area)

**Percentage of Households with Joint Tour**

Also the result of the CDAP model, summarized for each household.

**Mandatory Tour Frequency**

Result of the mandatory tour frequency model, summarized for each person with a daily activity pattern type _M_

**Tour rate by person type**

Summary of tours per person resulting from all tour generation models. Joint tours are counted for each participant.

**Individual non-mandatory tour frequency**

Results of individual non-mandatory tour frequency model, summarized for each person with a daily activity pattern type _M_ or _N_.

Chart Column 1 {data-width=160}
--------------------------------------------

### Daily Activity Pattern{data-height=500}
```{r Hist_DAP}
base_df <- base_data[[which(base_csv_names=="dapSummary_vis")]]
base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)]
base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char)
base_df$DAP <- factor(base_df$DAP, levels = dap_types)
build_df <- build_data[[which(build_csv_names=="dapSummary_vis")]]
build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)]
build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char)
build_df$DAP <- factor(build_df$DAP, levels = dap_types)

base_df$grp <- BASE_SCENARIO_NAME
build_df$grp <- BUILD_SCENARIO_NAME
colnames(build_df) <- colnames(base_df)

sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="DAP", y = c("freq"), grp = "PERNAME", shared = T)
p <- plotly_bar_plotter(data = sd.pertype, height = 250, xlabel = "DAP", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T)

bscols(widths=c(3,9),
  list(
    filter_select("pertype_dap", "Select Person Type", sd.pertype, ~grp_var,multiple=F)),
    p
  )

```

### Percentage of Households with a Joint Tour{data-height=300}
```{r Hist_Presence_Joint}
base_pos <- which(base_csv_names=="hhsizeJoint")
base_df <- base_data[[base_pos]]
base_df <- base_df %>%
  group_by(HHSIZE) %>%
  mutate(percent = prop.table(freq)) %>%
  filter(JOINT==1) %>%
  ungroup()
build_pos <- which(build_csv_names=="hhsizeJoint")
build_df <- build_data[[build_pos]]
build_df <- build_df %>%
  group_by(HHSIZE) %>%
  mutate(percent = prop.table(freq)) %>%
  filter(JOINT==1) %>%
  ungroup()

colnames(build_df) <- colnames(base_df)

std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "HHSIZE", y = c("percent"))
p <- plotly_bar_plotter(data = std_DF, xlabel = "HH Size", ylabel = "Percent of Households", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = F, tickformat = "%", hoverformat = ".1%")
p

```

### Mandatory Tour Frequency{data-height=500}
```{r Hist_MTF}
base_pos <- which(base_csv_names=="mtfSummary_vis")
base_df <- base_data[[base_pos]]
base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)]
base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char)
base_df$mtf_name <- mtf_df$name[match(base_df$MTF, mtf_df$code)]
base_df$mtf_name <- factor(base_df$mtf_name, levels = mtf_names)
build_pos <- which(build_csv_names=="mtfSummary_vis")
build_df <- build_data[[build_pos]]
build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)]
build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char)
build_df$mtf_name <- mtf_df$name[match(build_df$MTF, mtf_df$code)]
build_df$mtf_name <- factor(build_df$mtf_name, levels = mtf_names)
colnames(build_df) <- colnames(base_df)

sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="mtf_name", y = c("freq"), grp = "PERNAME", shared = T)
p <- plotly_bar_plotter(data = sd.pertype, height = 250, xlabel = "MTF Choice", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickangle = -30, bottom_offset = 50)

bscols(widths=c(3,9),
  list(
    filter_select("pertype_mtf", "Select Person Type", sd.pertype, ~grp_var,multiple=F)),
    p
  )

```

Chart Column 1 {data-width=150}
--------------------------------------------
### Total Tour Rate (only active Persons)
```{r Hist_totaltours}
base_df <- base_data[[which(base_csv_names=="total_tours_by_pertype_vis")]]
base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)]
base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char)
base_df1 <- base_data[[which(base_csv_names=="activePertypeDistbn")]]
base_df$persons <- base_df1$freq[match(base_df$PERTYPE, base_df1$PERTYPE)]
base_df$tourrate <- round(base_df$freq/base_df$persons,2)

build_df <- build_data[[which(build_csv_names=="total_tours_by_pertype_vis")]]
build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)]
build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char)
build_df1 <- build_data[[which(build_csv_names=="activePertypeDistbn")]]
build_df$persons <- build_df1$freq[match(build_df$PERTYPE, build_df1$PERTYPE)]
build_df$tourrate <- round(build_df$freq/build_df$persons,2)

colnames(build_df) <- colnames(base_df)

std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "PERNAME", y = c("tourrate"))
p <- plotly_bar_plotter(data = std_DF, xlabel = "Person Type", ylabel = "Tour Rate", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = F, height = 340, tickangle = -30, bottom_offset = 50)
p


```


### Persons by Individual Non-Mandatory Tours
```{r Hist_INM}
base_df <- base_data[[which(base_csv_names=="inmSummary_vis")]]
base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)]
base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char)

build_df <- build_data[[which(build_csv_names=="inmSummary_vis")]]
build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)]
build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char)

colnames(build_df) <- colnames(base_df)

sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="nmtours", y = c("freq"), grp = "PERNAME", shared = T)
#p <- plotly_bar_plotter(data = sd.pertype, height = 340, xlabel = "Number of Tours", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, #BUILD_SCENARIO_NAME), percent = T, tickvals = c(seq(0,2), "3pl"), ticktext = c("0", "1", "2", "3pl"))

p <- plotly_bar_plotter(data = sd.pertype, height = 340, xlabel = "Number of Tours", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T)

bscols(widths=c(3,9),
  list(
    filter_select("pertype_mtf", "Select Person Type", sd.pertype, ~grp_var,multiple=F)),
    p
  )


```



Joint Tours{data-navmenu="Tour Level"}
============================================

Description {.sidebar data-width=225}
--------------------------------------------

********

This page tabulates the results of the Joint Tour Frequency and Composition Model and the Joint Tour Person Participation Model.

**Joint Tour Frequency**

The frequency of households by number and purpose of joint tours.

**Joint Tour Composition**

The frequency of tours by composition (Adults only, Children only, Adults + Children).

**Joint Tour Party Size**

The frequency of joint tours by the number of household members participating in the tour.

**Joint Tours by HH Size**

The frequency of households by household size and the number of joint tours per household.

**Joint Tours by HH Size**

_Tour Level_

Distribution of joint tours by party size for each composition type.


Chart Column 1 {data-width=150}
--------------------------------------------
### Joint Tour Frequency{data-height=675}
```{r jtf}
base_df <- base_data[[which(base_csv_names=="jtf")]]
build_df <- build_data[[which(build_csv_names=="jtf")]]
# remove no joint tours option
base_df <- base_df[-1,]
build_df <- build_df[-1,]
colnames(build_df) <- colnames(base_df)

std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "alt_name", y = c("freq"))
std_DF$xvar <- factor(std_DF$xvar, levels = jtf_alternatives)

p <- plotly_bar_plotter(data = std_DF, xlabel = "Joint Tour Combination", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, height = 500, bottom_offset = 275, tickangle = 300)
p

```

### Joint Tour Composition
```{r jtf_comp}
base_df <- base_data[[which(base_csv_names=="jointComp")]]
names(base_df)[names(base_df)=="tour_composition"] <- "COMPOSITION"
build_df <- build_data[[which(build_csv_names=="jointComp")]]
colnames(build_df) <- colnames(base_df)

p1 <- plotly_pie_chart(data = base_df, label_var = "COMPOSITION", value_var = "freq", height = 250, title = BASE_SCENARIO_NAME, top_offset = 50)
p2 <- plotly_pie_chart(data = build_df, label_var = "COMPOSITION", value_var = "freq", height = 250, title = BUILD_SCENARIO_NAME, top_offset = 50)

bscols(widths=c(6,6),
  p1,
  p2
  )
```

Chart Column 1 {data-width=150}
--------------------------------------------

### Joint Tours By Number of Household Members
```{r jtf_partysize}
base_df <- base_data[[which(base_csv_names=="jointPartySize")]]
build_df <- build_data[[which(build_csv_names=="jointPartySize")]]
colnames(build_df) <- colnames(base_df)

build_df$freq[build_df$NUMBER_HH==5] <- sum(build_df$freq[build_df$NUMBER_HH>=5])
build_df <- build_df[build_df$NUMBER_HH<=5, ]

std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "NUMBER_HH", y = c("freq"))
p <- plotly_bar_plotter(data = std_DF, xlabel = "Joint Party Size", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, height = 200)
p

```

### Joint Tours by Household Size
```{r jtf_byhhsize}
base_pos <- which(base_csv_names=="jointToursHHSize")
base_df <- base_data[[base_pos]]

build_pos <- which(build_csv_names=="jointToursHHSize")
build_df <- build_data[[build_pos]]
colnames(build_df) <- colnames(base_df)

sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="jointTours", y = c("freq"), grp = "hhsize", shared = T)
p <- plotly_bar_plotter(data = sd.pertype, height = 225, xlabel = "Number of Joint Tours", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T)

bscols(widths=c(3,9),
  list(
    filter_select("jtf_hhsize", "Select HH Size Group", sd.pertype, ~grp_var,multiple=F)),
    p
  )

```

### Party Size Distribution by Joint Tour Composition
```{r jtf_comppartysize}
base_df <- base_data[[which(base_csv_names=="jointCompPartySize")]]
build_df <- build_data[[which(build_csv_names=="jointCompPartySize")]]
colnames(build_df) <- colnames(base_df)

sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="partysize", y = c("freq"), grp = "comp", shared = T)
p <- plotly_bar_plotter(data = sd.pertype, height = 225, xlabel = "Joint Party Size", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T)

bscols(widths=c(3,9),
  list(
    filter_select("jtf_comp", "Select Party Composition", sd.pertype, ~grp_var,multiple=F)),
    p
  )

```




Destination{data-navmenu="Tour Level"}
============================================

Description {.sidebar data-width=225}
--------------------------------------------

********

**Non-Mandatory Tour Length Distribution**

Results of non-mandatory tour destination choice models. 

Distribution of tours by distance between tour origin and destination for each non-mandatory tour purpose.


Chart Column 1 {data-width=100}
--------------------------------------------
### Non-Mandatory Tour Length Distribution{data-height=350}
```{r nm_tlfd}
base_df <- base_data[[which(base_csv_names=="tourDistProfile_vis")]]
build_df <- build_data[[which(build_csv_names=="tourDistProfile_vis")]]
colnames(build_df) <- colnames(base_df)

# change purpose names to standard format
base_df$PURPOSE <- as.character(base_df$PURPOSE)
build_df$PURPOSE <- as.character(build_df$PURPOSE)
base_df$PURPOSE <- purpose_type_df$name[match(base_df$PURPOSE, purpose_type_df$code)]
build_df$PURPOSE <- purpose_type_df$name[match(build_df$PURPOSE, purpose_type_df$code)]

sd.purpose <- get_sharedData(data_df1 = base_df, data_df2 = build_df, run1_name = BASE_SCENARIO_NAME, 
                             run2_name = BUILD_SCENARIO_NAME, x = "distbin", y = c("freq"), grp = "PURPOSE")

p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Miles", percent = T, 
                             tickvals = seq(2,41), ticktext = c(seq(1,40), "40pl"))
bscols(widths=c(2,10),
  filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F),
  p1
  )
```

### Average Non-Mandatory Tour Lengths (Miles){data-height=250}
```{r Table1_nonMandTripLength}
base_df <- base_data[[which(base_csv_names=="nonMandTripLengths")]]
build_df <- build_data[[which(build_csv_names=="nonMandTripLengths")]]
df <- data.frame(base_df, build_df[,-1])
colnames(df) <- c("Purpose", BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME)
df$Purpose <- purpose_type_df$name[match(df$Purpose, purpose_type_df$code)]

t1 <- kable(df, format = "html", digits = 2, row.names = F, align = 'c', format.args = list(big.mark = ',')) %>%
  kable_styling("striped", full_width = F)
t1
```


TOD {data-navmenu="Tour Level"}
============================================

Description {.sidebar data-width=200}
--------------------------------------------

********

**Tour Departure Arrival & Duration**

Tour Time-of-day Choice Model results.

Each tour is assigned a time period of departure (time leaving home or work) and arrival (time arriving back at home or work). The entire day is divided into 40 half-hour bins (the first bin includes 3:00 AM to 5:00 AM and the last bin includes 12:00 PM to 3:00 AM).

Tour duration is calculated as a function of departure and arrival period. It includes travel time and time spent at the primary destination and all intermediate stops.

Results are shown for tours, filtered by tour purpose.

********

**Aggregate Tour Arrival-Departure**

_EA_: 3:00 AM to 6:00 AM

_AM_: 6:00 AM to 9:00 AM

_MD_: 9:00 AM to 3:30 PM

_PM_: 3:30 PM to 7:00 PM

_EV_: 7:00 PM to 3:00 AM

Chart Column 1 {.tabset}
--------------------------------------------
### Tour Departure-Arrival Profile
```{r tour_tod}
base_df <- base_data[[which(base_csv_names=="todProfile_vis")]]
base_df$tod_bin <- tod_df$bin[match(base_df$id, tod_df$id)]
base_df$dur_bin <- dur_df$bin[match(base_df$id, dur_df$id)]
build_df <- build_data[[which(build_csv_names=="todProfile_vis")]]
build_df$tod_bin <- tod_df$bin[match(build_df$id, tod_df$id)]
build_df$dur_bin <- dur_df$bin[match(build_df$id, dur_df$id)]
colnames(build_df) <- colnames(base_df)

# change purpose names to standard format
base_df$purpose <- as.character(base_df$purpose)
build_df$purpose <- as.character(build_df$purpose)
base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)]
build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)]

sd.purpose <- get_sharedData(data_df1 = base_df, data_df2 = build_df, run1_name = BASE_SCENARIO_NAME, 
                             run2_name = BUILD_SCENARIO_NAME, x = "id", y = c("freq_dep", "freq_arr", "freq_dur"), grp = "purpose")

p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Tour Departure", percent = T, left_offset = 25, 
                             tickvals = seq(1,40), ticktext = todBins, bottom_offset = 150, tickangle = 315, height = 275)
p2 <- plotly_density_plotter(sd.purpose, index = "two", xlabel = "Tour Arrival", percent = T, left_offset = 25, 
                             tickvals = seq(1,40), ticktext = todBins, bottom_offset = 150, tickangle = 315, height = 275)
p3 <- plotly_density_plotter(sd.purpose, index = "three", xlabel = "Tour Duraction", percent = T, left_offset = 25, 
                             tickvals = seq(1,40), ticktext = durBins, bottom_offset = 50, tickangle = 315, height = 225)
	
bscols(widths=c(2,10),
  filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F),
  list(p1, p2, p3)
  )


```

### Tour Aggregate Departure-Arrival Profile
```{r tour_tod_agg}
base_df <- base_data[[which(base_csv_names=="todProfile_vis")]]
base_df$tod_agg <- cut(base_df$id, breaks = timePeriodBreaks, labels = timePeriods, right = FALSE)
base_df <- base_df %>%
  group_by(purpose, tod_agg) %>%
  summarise(freq_dep = sum(freq_dep), freq_arr = sum(freq_arr), freq_dur = sum(freq_dur)) %>%
  ungroup()

build_df <- build_data[[which(build_csv_names=="todProfile_vis")]]
build_df$tod_agg <- cut(build_df$id, breaks = timePeriodBreaks, labels = timePeriods, right = FALSE)
build_df <- build_df %>%
  group_by(purpose, tod_agg) %>%
  summarise(freq_dep = sum(freq_dep), freq_arr = sum(freq_arr), freq_dur = sum(freq_dur)) %>%
  ungroup()
colnames(build_df) <- colnames(base_df)

# change purpose names to standard format
base_df$purpose <- as.character(base_df$purpose)
build_df$purpose <- as.character(build_df$purpose)
base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)]
build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)]

sd.purpose <- get_standardDF(data_df1=base_df, data_df2=build_df, x="tod_agg", y = c("freq_dep", "freq_arr", "freq_dur"), grp = "purpose", shared = T)

p1 <- plotly_bar_plotter(data = sd.purpose, height = 350, xlabel = "Tour Departure", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T)
p2 <- plotly_bar_plotter(data = sd.purpose, height = 350, xlabel = "Tour Arrival", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, index = 2)

bscols(widths=c(2,10),
  filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F),
  list(p1, p2)
  )


```



Tour Mode{data-navmenu="Tour Level"}
============================================


Chart Column 1{data-width=150}
--------------------------------------------


### Tour Mode Choice
```{r tourMode}
base_df <- base_data[[which(base_csv_names=="tmodeProfile_vis")]]
base_df$purpose <- as.character(base_df$purpose)
base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)]
build_df <- build_data[[which(build_csv_names=="tmodeProfile_vis")]]
build_df$purpose <- as.character(build_df$purpose)
build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)]
colnames(build_df) <- colnames(base_df)


sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="id", y = c("freq_as0", "freq_as1", "freq_as2", "freq_all"), grp = "purpose", shared = T)
p1 <- plotly_bar_plotter(data = sd.pertype, height = 375, xlabel = "Tour Mode [Zero Auto]", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,9), ticktext = tourMode, bottom_offset = 55, tickangle = 300)
p2 <- plotly_bar_plotter(data = sd.pertype, height = 375, xlabel = "Tour Mode [Autos < HH Size]", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,9), ticktext = tourMode, index = 2, bottom_offset = 55, tickangle = 300)
p3 <- plotly_bar_plotter(data = sd.pertype, height = 375, xlabel = "Tour Mode [Autos >= HH Size]", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,9), ticktext = tourMode, index = 3, bottom_offset = 55, tickangle = 300)
p4 <- plotly_bar_plotter(data = sd.pertype, height = 375, xlabel = "Tour Mode [Total]", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,9), ticktext = tourMode, index = 4, bottom_offset = 55, tickangle = 300)

filter_select("tourMode", "Select Tour Purpose", sd.pertype, ~grp_var,multiple=F)

```

********


**Tour Mode Choice**

Results of Tour Mode Choice Models, which selects a primary mode for each tour. 

Distribution of tours by tour mode and the ratio of autos to drivers in the household.


Chart Column 2 {data-width=400}
--------------------------------------------

### 
```{r tourMode2}
bscols(widths=c(12),
  list(p1,p2)
  )
```

Chart Column 3 {data-width=400}
--------------------------------------------

### 
```{r tourMode3}
bscols(widths=c(12),
  list(p3,p4)
  )
```


Stop Frequency {data-navmenu="Trip Level"}
============================================

Description {.sidebar data-width=175}
--------------------------------------------

********

**Stop Frequency**

Results of the Intermediate Stop Frequency Model, which predicts the number of intermediate stops on each tour by tour direction (outbound versus inbound).

The summary shows percent of tours by number of stops on the tour and tour direction.

**Stop Purpose**

Results of the Intermediate Stop Purpose Model, which is currently implemented as a Monte Carlo choice according to probability distributions generated from survey data.

The summary shows the percent of intermediate stops by stop purpose and tour purpose.

Chart Column 1 {data-width=200}
--------------------------------------------
### Stop Frequency - Directional
```{r stopfreq_dir}
base_df <- base_data[[which(base_csv_names=="stopfreqDir_vis")]]
build_df <- build_data[[which(build_csv_names=="stopfreqDir_vis")]]
colnames(build_df) <- colnames(base_df)

# change purpose names to standard format
base_df$purpose <- as.character(base_df$purpose)
build_df$purpose <- as.character(build_df$purpose)
base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)]
build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)]


sd.pertype1 <- get_standardDF(data_df1=base_df, data_df2=build_df, x="nstops", y = c("freq_out", "freq_inb"), grp = "purpose", shared = T)
p1 <- plotly_bar_plotter(data = sd.pertype1, height = 325, xlabel = "Number of Stops - Outbound", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,4), ticktext = c("0", "1", "2", "3pl"))
p2 <- plotly_bar_plotter(data = sd.pertype1, height = 325, xlabel = "Number of Stops - Inbound", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,4), ticktext = c("0", "1", "2", "3pl"), index = 2)
bscols(widths=c(12),
  list(
    filter_select("stopfreq_dir", "Select Tour Purpose", sd.pertype1, ~grp_var,multiple=F), 
    p1, 
    p2)
  )

```



Chart Column 1 {data-width=300}
--------------------------------------------
### Stop Frequency - Total{data-height=250}
```{r stopfreq_total}
base_df <- base_data[[which(base_csv_names=="stopfreq_total_vis")]]
build_df <- build_data[[which(build_csv_names=="stopfreq_total_vis")]]
colnames(build_df) <- colnames(base_df)

# change purpose names to standard format
base_df$purpose <- as.character(base_df$purpose)
build_df$purpose <- as.character(build_df$purpose)
base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)]
build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)]


sd.pertype2 <- get_standardDF(data_df1=base_df, data_df2=build_df, x="nstops", y = c("freq"), grp = "purpose", shared = T)
p1 <- plotly_bar_plotter(data = sd.pertype2, height = 350, xlabel = "Number of Stops", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,6), ticktext = c("0", "1", "2", "4", "5", "6pl"))

bscols(widths=c(3,9),
  list(
    filter_select("stopfreq_total", "Select Tour Purpose", sd.pertype2, ~grp_var,multiple=F)),
    p1
  )
```

### Stop Purpose by Tour Purpose{data-height=250}
```{r stoppurp_tourpurp}
base_df <- base_data[[which(base_csv_names=="stoppurpose_tourpurpose_vis")]]
build_df <- build_data[[which(build_csv_names=="stoppurpose_tourpurpose_vis")]]
colnames(build_df) <- colnames(base_df)

# change purpose names to standard format
base_df$purpose <- as.character(base_df$purpose)
build_df$purpose <- as.character(build_df$purpose)
base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)]
build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)]


sd.pertype3 <- get_standardDF(data_df1=base_df, data_df2=build_df, x="stop_purp", y = c("freq"), grp = "purpose", shared = T)
p1 <- plotly_bar_plotter(data = sd.pertype3, height = 350, xlabel = "Stop Purpose", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,10), ticktext = stopPurposes)

bscols(widths=c(3,9),
  list(
    filter_select("stoppurp_tourpurp", "Select Tour Purpose", sd.pertype3, ~grp_var,multiple=F)),
    p1
  )

```


Location{data-navmenu="Trip Level"}
============================================

Description {.sidebar data-width=175}
--------------------------------------------

********

**Stop Location**

Results of the Intermediate Stop Location Choice Model, which predicts the location of each intermediate stop.

The summary shows the distribution of intermediate stops by out of direction distance and tour purpose.

Out of direction distance is defined as the extra distance to the destination as a result of traveling through the stop location. 
For stops in the outbound direction, it is based on the distance between the last known location (the tour origin or previous outbound stop) and the tour primary destination.
For stops in the inbound direction, it is based on the distance between the last known location (the tour primary destination or previous inbound stop) and the tour origin.

Chart Column 1 {data-width=800}
--------------------------------------------

### Stop Location - Out of Direction Distance{data-height=350}
```{r stopDC}
base_df <- base_data[[which(base_csv_names=="stopDC_vis")]]
build_df <- build_data[[which(build_csv_names=="stopDC_vis")]]
colnames(build_df) <- colnames(base_df)

# change purpose names to standard format
base_df$PURPOSE <- as.character(base_df$PURPOSE)
build_df$PURPOSE <- as.character(build_df$PURPOSE)
base_df$PURPOSE <- purpose_type_df$name[match(base_df$PURPOSE, purpose_type_df$code)]
build_df$PURPOSE <- purpose_type_df$name[match(build_df$PURPOSE, purpose_type_df$code)]


sd.purpose <- get_sharedData(data_df1 = base_df, data_df2 = build_df, run1_name = BASE_SCENARIO_NAME, 
                             run2_name = BUILD_SCENARIO_NAME, x = "distbin", y = c("freq"), grp = "PURPOSE")

p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Out of Direction Distance (Miles)", percent = T, left_offset = 25, 
                             tickvals = seq(1,42), ticktext = outDirDist, height = 600, tickangle = 300, bottom_offset = 50)
bscols(widths=c(12),
  list(
    filter_select("stopDC", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), p1)
  )
```

Chart Column 1 {data-width=300}
--------------------------------------------

### Average Out of Direction Distance (Miles){data-height=250}
```{r Table1_outOfDir}
base_df <- base_data[[which(base_csv_names=="avgStopOutofDirectionDist_vis")]]
build_df <- build_data[[which(build_csv_names=="avgStopOutofDirectionDist_vis")]]
df <- data.frame(base_df, build_df[,-1])
colnames(df) <- c("Tour_Purpose", BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME)
df$Tour_Purpose <- purpose_type_df$name[match(df$Tour_Purpose, purpose_type_df$code)]
#
#t1 <- kable(df, format = "html", digits = 2, row.names = F, align = 'c', format.args = list(big.mark = ',')) %>%
#  kable_styling("striped", full_width = F)

t1 <- htmlTable(txtRound(df, 2), 
                align = "c|r",
                rnames = F,
                col.columns = c(rep("#E6E6F0", 1),
                          rep("none", ncol(df) - 1)), 
                caption = "_______________________________________________________")

t1
```


TOD{data-navmenu="Trip Level"}
============================================

Description {.sidebar data-width=175}
--------------------------------------------

********

**Stop Departure**

Results of the Stop Departure Time Choice Model. The departure time of each stop on the tour is currently implemented as a Monte Carlo choice of time period from distributions generated from survey data.

The entire day is divided into 40 half-hour bins (The first bin includes 3:00 AM to 5:00 AM and the last bin includes 12:00 PM to 3:00 AM).

**Trip Departure**

Summarizes all trips by departure time period, including trips to and from intermediate stops and the tour primary destination.

Chart Column 1 {.tabset}
--------------------------------------------

### Stop & Trip Departure{data-height=650}
```{r stopDep}
base_df <- base_data[[which(base_csv_names=="stopTripDep_vis")]]
build_df <- build_data[[which(build_csv_names=="stopTripDep_vis")]]
colnames(base_df) <- c("timebin", "purpose", "freq_stop", "freq_trip")
colnames(build_df) <- colnames(base_df)

# change purpose names to standard format
base_df$purpose <- as.character(base_df$purpose)
build_df$purpose <- as.character(build_df$purpose)
base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)]
build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)]


sd.purpose <- get_sharedData(data_df1 = base_df, data_df2 = build_df, run1_name = BASE_SCENARIO_NAME, 
                             run2_name = BUILD_SCENARIO_NAME, x = "timebin", y = c("freq_stop", "freq_trip"), grp = "purpose")

p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Stop Departure", percent = T, left_offset = 25,
                             tickvals = seq(1,40), ticktext = todBins, bottom_offset = 150, tickangle = 315, height = 400)
p2 <- plotly_density_plotter(sd.purpose, index = "two", xlabel = "Trip Departure", percent = T, left_offset = 25, 
                             tickvals = seq(1,40), ticktext = todBins, bottom_offset = 150, tickangle = 315, height = 400)
#p3 <- datatable(sd.purpose$data())
bscols(widths=c(2,10),
  filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F),
  list(p1, p2)
  )
```

### Aggregate Stop & Trip Departure
```{r trip_tod_agg}
base_df <- base_data[[which(base_csv_names=="stopTripDep_vis")]]
colnames(base_df) <- c("id","purpose","freq_stop","freq_trip")
base_df$tod_agg <- cut(base_df$id, breaks = timePeriodBreaks, labels = timePeriods, right = FALSE)
base_df <- base_df %>%
  group_by(purpose, tod_agg) %>%
  summarise(freq_stop = sum(freq_stop), freq_trip = sum(freq_trip)) %>%
  ungroup()

build_df <- build_data[[which(build_csv_names=="stopTripDep_vis")]]
colnames(build_df) <- c("id","purpose","freq_stop","freq_trip")
build_df$tod_agg <- cut(build_df$id, breaks = timePeriodBreaks, labels = timePeriods, right = FALSE)
build_df <- build_df %>%
  group_by(purpose, tod_agg) %>%
  summarise(freq_stop = sum(freq_stop), freq_trip = sum(freq_trip)) %>%
  ungroup()
colnames(build_df) <- colnames(base_df)

# change purpose names to standard format
base_df$purpose <- as.character(base_df$purpose)
build_df$purpose <- as.character(build_df$purpose)
base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)]
build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)]

sd.purpose <- get_standardDF(data_df1=base_df, data_df2=build_df, x="tod_agg", y = c("freq_stop", "freq_trip"), grp = "purpose", shared = T)

p1 <- plotly_bar_plotter(data = sd.purpose, height = 350, xlabel = "Stop Departure", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T)
p2 <- plotly_bar_plotter(data = sd.purpose, height = 350, xlabel = "Trip Departure", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, index = 2)

bscols(widths=c(2,10),
  filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F),
  list(p1, p2)
  )


```



Trip Mode{data-navmenu="Trip Level"}
============================================



Chart Column 1 {data-width=125}
--------------------------------------------

###  {data-height=200}

***Trip Mode Choice***

The results of the Trip Mode Choice Model, which predicts the mode of each trip on the tour.

Distribution of trips by trip mode and tour mode, which constrains the availability of each trip mode and influences the utility of each available trip mode.

### Trip Mode Choice
```{r tripMode}
base_df <- base_data[[which(base_csv_names=="tripModeProfile_vis")]]
build_df <- build_data[[which(build_csv_names=="tripModeProfile_vis")]]
colnames(build_df) <- colnames(base_df)

# change purpose names to standard format
base_df$purpose <- as.character(base_df$purpose)
build_df$purpose <- as.character(build_df$purpose)
base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)]
build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)]


sd.purpose <- get_standardDF(data_df1=base_df, data_df2=build_df, x="tripmode", y = c("value"), grp = "grp_var", shared = T)

p <- plotly_bar_plotter(data = sd.purpose, height = 700, xlabel = "Trip Mode", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,9), ticktext = tripMode, bottom_offset = 75)

bscols(widths=c(12),
  list(filter_select("tripMode1", "Select Tour Purpose", sd.purpose, ~purpose,multiple=F), 
       filter_select("tripMode1", "Select Tour Mode", sd.purpose, ~tourmode,multiple=F))
  )
```

Chart Column 2 {data-width=800}
--------------------------------------------
###
```{r tripMode2}
bscols(widths=c(12),
  list(p)
  )

```

Count vs Volume{data-navmenu="Assignment"}
============================================

Description {.sidebar data-width=175}
--------------------------------------------

********

**Link level count comparison**

Results of auto assignment.

Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period.


Chart Column 2{.tabset}
--------------------------------------------

### Count vs Volume by Facility Type{data-height=575}
```{r count_vol1}


```

### Count vs Volume - All Links{data-height=575}
```{r count_vol2}

```

### Gap Statistics{data-height=575}
```{r count_vol3}


```